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Digital Health (ICDH), IEEE International Conference on

作者： Vince S. Siu Kuan Yu Hsieh Italo Buleje Takashi Itoh Tian Hao Ben Civjan Nigel Hinds Bing Dang Jeffrey L. Rogers Bo Wen Digital Health IBM T.J. Watson Research Center Yorktown Heights NY USA Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Digital Health IBM Research Tokyo Japan Illinois Computer Science University of Illinois Urbana-Champaign Urbana-Champaign IL USA Emerging Technology Engineering IBM T.J. Watson Research Center Yorktown Heights USA

Artificial intelligence (AI) has shown great promise in revolutionizing the field of digital health by improving disease diagnosis, treatment, and prevention. This paper describes the Health Guardian platform, a non-commercial, scientific research-based platform developed by the IBM Digital Health team to rapidly translate AI research into cloud-based microservices. The platform can collect health-related data from various digital devices, including wearables and mobile applications. Its flexible architecture supports microservices that accept diverse data types such as text, audio, and video, expanding the range of digital health assessments and enabling holistic health evaluations by capturing voice, facial, and motion bio-signals. These microservices can be deployed to a clinical cohort specified through the Clinical Task Manager (CTM). The CTM then collects multi-modal, clinical data that can iteratively improve the accuracy of AI predictive models, discover new disease mechanisms, or identify novel biomarkers. This paper highlights three microservices with different input data types, including a text-based microservice for depression assessment, a video-based microservice for sit-to-stand mobility assessment, and a wearable-based microservice for functional mobility assessment. The CTM is also discussed as a tool to help design and set up clinical studies to unlock the full potential of the platform. Today, the Health Guardian platform is being leveraged in collaboration with research partners to optimize the development of AI models by utilizing a multitude of input sources. This approach streamlines research efforts, enhances efficiency, and facilitates the development and validation of digital health applications.

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Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay

Bayesian Optimization of Double-SAC Robotic Arm Path Plannin...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuai Yuan Ke Wen Feng Zhang Jianqiang Xiong School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shengyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shengyang China

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance.

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Fire evacuation situation recognition and evacuation route search scheme of 5G-based mobile system

Fire evacuation situation recognition and evacuation route s...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Mahnsuk Yoon Gilhwan Lim Jaemin Lee Hyunchul Choi Hyunjoo Lee Kyucheol Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Research Institute Daon Co. Ltd. Gumi Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

This paper introduces a comprehensive system and associated technologies designed to facilitate fire evacuation and minimize the risk of secondary accidents such as battery explosions in mobile systems utilizing 5G communication technologies. As the use of mobile systems like kiosks and vacuum cleaners becomes widespread in multi-use facilities, the potential risks associated with their large-capacity batteries, especially during fire emergencies, have become a significant concern. In densely populated environments, if these mobile systems are effectively utilized, they can guide evacuation routes during a fire, significantly reducing human and property damage. The proposed approach uses a multi-sensor-based fire detector that leverages 5G communication technology for real-time fire detection. Upon detection of a fire, the system determines the degree of spread of substances harmful to humans and, in conjunction with the mobile system, guides individuals along evacuation routes to safe areas. This technique operates through the integration of fire detectors, a fire situation awareness server, and the mobile system using 5G communication technology. The proposed technology operates in such a way that, leveraging building and fire map data, the mobile system situated in the area receives the optimal path to the emergency exit from the fire situation awareness server, and guides the evacuation route accordingly. The performance of the proposed technology, with respect to its collaborative operation through fire detection sensors, has been validated in a simulation environment assuming a fire incident.

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Proposal of Axial-gap type Magnetic Multiple Spur Gear for In-wheel Motor System of Electric Vehicle

Proposal of Axial-gap type Magnetic Multiple Spur Gear for I...

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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Taiga Kamijo Kohei Aiso Kan Akatsu Yasuaki Aoyama Electrical Engineering and Computer Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan Department of Faculty of Engineering Division of Intelligent Systems Engineering Yokohama National University Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

Magnetic gears have the characteristics of maintenance-free, low noise, and low vibration due to non-contact power transmission. By taking advantage of the non-contact power transmission, the motor system can be made smaller and lighter by further increasing the motor speed. Magnetic Multiple Spur Gear (MMSG) has been proposed, the drive system of MMSG and multiple high-speed motors can reduce the size and weight of the drive system by converting the total power of multiple high-speed motors. Applying this drive system to in-wheel motors will increase the available interior space in electric vehicles. However, there is a risk that the high-speed rotor and low-speed rotor contact with each other due to the radial structure in the in-wheel motor system. In this research, an axial-gap MMSG is proposed to solve the problem and to obtain higher torque density, the performances such as torque density and efficiency are evaluated by finite element analysis.

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A HOLISTIC VIEW ON DATA PROTECTION FOR SHARING, COMMUNICATING, AND COMPUTING ENVIRONMENTS: TAXONOMY AND FUTURE DIRECTIONS

arXiv

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arXiv 2022年

作者： Gupta, Ishu Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System’s analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection. © 2022, CC BY.

关键词： Digital storage

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Target Detection in Complex Environments using UAV and Deep Learning-based Focal Pruning Method

Target Detection in Complex Environments using UAV and Deep ...

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Emerging research in Computational Science (ICERCS), International Conference on

作者： Sara H. Thejeel Ahmed Hussian Wasan Saad Ahmed Sajad Ali Zearah Mohsin Ali Department Of Medical Devices Engineering Technologies National University Of Science And Technology Dhi Qar Nasiriyah Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq Computer Technologies Engineering AlTurath University College Baghdad Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq Computer Technology Engineering Al-Maamoun University College Baghdad Iraq

Applications from computer vision that use deep learning for target detection have become incredibly popular recently. The requirement for future applications includes monitoring, visual navigation, object identification, and sensor-based preventative planning, together with the growing UAV industry advances, which hold great potential for deep learning. Deep learning techniques for object detection have swiftly evolved into a rendering tool for photos taken by flying drones. This paper's primary goal is to present a comprehensive overview of contemporary deep learning-based object recognition algorithms and to explore their most recent contributions to UAV low-altitude datasets. As opposed to standard or remote-sensor datasets, which comprise a smaller percentage of the literature, low-level UAV datasets are the focus of the research. To conduct real-time bridge crack diagnosis with Unmanned Aerial Vehicles (UAV), a deep learning system dubbed the You Only Look Once version-4 Focal Pruning Method (YOLOv4-FPM) is suggested based on the YOLOv4 model. For YOLOv4-FPM, focal loss architecture is utilized to streamline the loss feature, improve accuracy and colour transmission, and solve issues with dynamic context. A pruning method is used to speed up detection and facilitate the network. The outcomes show that the model can successfully handle the difficulty given by various backdrops, and the detection velocity is 97.2% effective enough for the real-time detection mission. The YOLOv4-FPM of deep learning technology guarantees 96.3% detection accuracy compared to current drone detection methods.

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Shared Incentive System for Clinical Pathway Experience

Shared Incentive System for Clinical Pathway Experience

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiqi Dai Wenhao Zhao Xia Xie Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.

关键词： Privacy Hospitals Collaboration Communication channels Data models Security Incentive schemes

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Auxiliary Informatics System: An Advancement Towards A Smart Home Environment

TechRxiv

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TechRxiv 2022年

作者： Gupta, Ishu Yadav, Preetesh K. Pareek, Sourav Shakeel, Saif Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The main problem that we face today is not about getting services, but how efficiently we manage them. We are living in an era where we cannot neglect the fact that we are now depending on various electronic devices which we use daily in our life. These devices have become an inseparable part of our life, in the present scenario technology is evolving so rapidly that it’s a fact if we do not accept any upcoming trend then we lag behind in the eye of others. Managing all these devices and remembering to tune them is quite a hectic task, as we have to do other main tasks too. Using every device efficiently at the same time is not possible. Hence, we came up with a solution, with a system that will manage all these devices according to predefined rules or instructions. Current smart home systems work in a way, to establish a connection between nodes and a hub and are controlled by smartphones, computers, remote, etc. In this research paper, we have proposed a model that will not only establish a connection but will work smartly according to instructions and algorithms saved earlier. This Auxiliary Informatics System (AuxIS) will serve the requirement of managing all the devices in a parallel fashion with very little human intervention and hence, will make a smarter home environment. © 2022, CC BY.

关键词： Internet of things

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MLRM: A MULTIPLE LINEAR REGRESSION BASED MODEL FOR AVERAGE TEMPERATURE PREDICTION OF A DAY

arXiv

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arXiv 2022年

作者： Gupta, Ishu Mittal, Harsh Rikhari, Deepak Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

Weather is a phenomenon that affects everything and everyone around us on a daily basis. Weather prediction has been an important point of study for decades as researchers have tried to predict the weather and climatic changes using traditional meteorological techniques. With the advent of modern technologies and computing power, we can do so with the help of machine learning techniques. We aim to predict the weather of an area using past meteorological data and features using the Multiple Linear Regression Model. The performance of the model is evaluated and a conclusion is drawn. The model is successfully able to predict the average temperature of a day with an error of 2.8 degrees Celsius. © 2022, CC BY.

关键词： Weather forecasting

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HISA-SMFM: HISTORICAL AND SENTIMENT ANALYSIS BASED STOCK MARKET FORECASTING MODEL

arXiv

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arXiv 2022年

作者： Gupta, Ishu Madan, Tarun Kumar Singh, Sukhman Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately. © 2022, CC BY.

关键词： Sentiment analysis

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